| Study Area (Satellite Data) [Ref.] | Method Used | Significant Conclusion | Comments |
| Larsemann Hills, Antarctic. (WV-2) [30] | 1. Lyzenga model 2. Stumpf model | Lyzenga model yielded better results than Stumpf model | The coastal band in the WV-2 plays an essential role in bathymetry |
| Coastal region near Mumbai, India. (RISAT-1 SAR) [64] | 1. Wave-tracing method 2. Linear dispersion relation | Using wave tracing method, the swell wavelength has been found to be in the range of 80 - 210 m. Using the Linear dispersion method the maximum swell wavelength was 210 m. | The wavelength decreases as the wave moves closer to the coast. |
| The lagoon of Venice. (QuickBird) [57] | Stratified genetic algorithm (SGA). | A very high correlation R2 = 0.96 was obtained with respect to in-situ data | SGA performs better than generic Jupp’s model |
| Wales, Alaska. (WV-2) [66] | Relative Water Depth in ENVI software. | A linear regression between measured and derived values of absolute depths resulted in R2 of 0.7221. | Locations with potentially high suspended sediments were found to cause discrepancies between in-situ and derived depth values. |
| Thessaloniki, Greece. (WV-2) [67] | Lyzenga model | Water depths measured: -Area with seagrass 2 - 6 m, -Area mixed with seagrass 2.4 - 6m, -Seagrass free area 6 - 15 m. | In all areas the majority of the estimated depths (73% - 76%), differed adequately from the soundings. |
| Strait southwest of the Singapore main island. (WV-2) [68] | Shallow Water Remote Sensing Reflectance Model | For water with a dark seabed, the Green band has the most depth sensitivity for depth up to about 5 m. In case of a bright seabed, the Red and Yellow bands are the most sensitive. | In cases where the depth sensitivity is low, the spectral bands are still useful for the derivation of water optical parameters. |
| The Southern coast of the island of Sardinia, Italy. (WV-2) [69] | Jupp Method a) IDL b) ENVI | Comparison of the two images being different in geometry and quality, the results coincide to a precision of 0.6 m. | A satisfactory correlation between in situ and derived depths was observed |
| Aquitaine, France. (SPOT-5) [15] | 1. Empirical calibration based on ground truth data 2. Semi-analytical model. | Semi-analytical model: The accuracy is satisfying (0.5 m) and not depth-dependent. Empirical model: The computed depths are under-estimated when water depth exceeds 2.5 m. The accuracy is depth- dependent getting worse with larger water depths | Empirical fitting is time-efficient, but Requires simultaneous high-density soundings. The semi-analytical approach can be implemented in any place without any ground truth data. |
| Kaneohe Bay, Oahu, Hawaii. (IKONOS, LIDAR) [20] | Lyzenga model | Water depths >20 m were obtained using Multibeam SONAR. Water depths <20 m were derived from an IKONOS image using Lyzenga method. | The most accurate method was simple, empirical multiple linear regression against known depths. |
| Eastern Banks, Moreton Bay. (QuickBird) [70] | 1. Lyzenga model 2. Stumpf model | Lyzenga algorithm was effectively used to map Water depth over the sand substrate type. An algorithm based on reflectance band ratios (Stumpf) was also tested separately on sand and seagrass substrate types. | Lyzenga algorithm could not be used to derive depth over seagrass substrate types. Stumpf algorithm was not able to effectively derive water depth on either substrate type |
| Naozhou Island in Guangdong, China. (SPOT-5) [71] | 1. Optical RS inversion technique for depth. 2. Depth inversion | The mean relative error of the depth segment ranges from 0 - 5 m. The dual-band model is the best of all the models used, its mean relative error is 22%, and its mean square error is 1.87 m. The model worked relatively well in the shallow water. | The multi-spectral image of SPOT-5 has the ability to inverse water depth, and its high resolution can describe more detail topographic information under water. |
| South China Sea. (WV-2, QuickBird) [72] | Lyzenga Model | Using Lyzenga model the error of water depth from QuickBird image is found to be about 9.7%. | The coastal blue band in WV-2 may retrieve more information. |
| Cancun and Hawaii, USA. (IKONOS, SHOALS LIDAR) [61] | Lyzenga Model | A single set of coefficients derived from a set of the IKONOS images gives good performance over a variety of conditions, with an aggregate RMS of 2.3 m over all of the data sets. | The algorithm corrects for a range of variations in both water attenuation and bottom reflectance using a linear combination of the log-transformed radiances in the blue and green channels. |
| Cape Verde Islands, Africa. (QuickBird, IKONOS) [73] | 1. Lyzenga Model 2. Jupp Model 3. Stumpf Model | The depth in coastal water (shallow water areas, depth < 30 m) with three different methodologies. | The results show that the ratio model is more robust in case of the non-homogeneous environment. |
| QingDaocity ShanDong province in China. (Landsat-TM) [74] | Bottom Reflection Model Based on Remote Sensing Bathymetry | The absolute values of negative linear relation coefficient −0.493425 is smaller than that derived from bottom classification. | The precision of bottom type classification using multi-spectral image information is better than single-band method. |
| Northwest Hawaiian Islands. (IKONOS, LIDAR) [55] | 1. Lyzenga Model 2. Stumpf Model | Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, and coral) and retrieve bathymetry in water depths of less than 10 - 15 m. | The ratio transform is more robust than the linear transform. |